


import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class 							lowercase  (						_UpperCAmelCase			):
     lowerCamelCase			:			List[Any]      = (CMStochasticIterativeScheduler,)
     lowerCamelCase			:			str      = 10







     def 							lowercase__       (						self :						Optional[Any]   ,   **_lowercase :						int      ):
           SCREAMING_SNAKE_CASE__       :  str						     =		{
               '''num_train_timesteps''': 2_01,
               '''sigma_min''': 0.002,
               '''sigma_max''': 80.0,
           }

           config.update(**_lowercase      )
           return config







     def 							lowercase__       (						self :						List[Any]      ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		10

           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__       :  Any						     =		self.scheduler_classes[0](**_lowercase      )

           scheduler.set_timesteps(_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		scheduler.timesteps[0]
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		scheduler.timesteps[1]

           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.dummy_sample
           SCREAMING_SNAKE_CASE__       :  str						     =		0.1 * sample

           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		scheduler.step(_lowercase   ,   _lowercase   ,   _lowercase      ).prev_sample
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		scheduler.step(_lowercase   ,   _lowercase   ,   _lowercase      ).prev_sample

           self.assertEqual(output_a.shape   ,   sample.shape      )
           self.assertEqual(output_a.shape   ,   output_a.shape      )







     def 							lowercase__       (						self :						str      ):
           for timesteps in [10, 50, 1_00, 10_00]:
                 self.check_over_configs(num_train_timesteps=_lowercase      )







     def 							lowercase__       (						self :						Tuple      ):
           for clip_denoised in [True, False]:
                 self.check_over_configs(clip_denoised=_lowercase      )







     def 							lowercase__       (						self :						Tuple      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__       :  Dict						     =		scheduler_class(**_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		1
           scheduler.set_timesteps(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  int						     =		scheduler.timesteps

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		torch.manual_seed(0      )

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.dummy_model()
           SCREAMING_SNAKE_CASE__       :  str						     =		self.dummy_sample_deter * scheduler.init_noise_sigma

           for i, t in enumerate(_lowercase      ):
                 # 1. scale model input
                 SCREAMING_SNAKE_CASE__       :  Any						     =		scheduler.scale_model_input(_lowercase   ,   _lowercase      )

                 # 2. predict noise residual
                 SCREAMING_SNAKE_CASE__       :  Dict						     =		model(_lowercase   ,   _lowercase      )

                 # 3. predict previous sample x_t-1
                 SCREAMING_SNAKE_CASE__       :  Any						     =		scheduler.step(_lowercase   ,   _lowercase   ,   _lowercase   ,   generator=_lowercase      ).prev_sample

                 SCREAMING_SNAKE_CASE__       :  List[Any]						     =		pred_prev_sample

           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		torch.sum(torch.abs(_lowercase      )      )
           SCREAMING_SNAKE_CASE__       :  int						     =		torch.mean(torch.abs(_lowercase      )      )

           assert abs(result_sum.item() - 192.7614      ) < 1E-2
           assert abs(result_mean.item() - 0.2510      ) < 1E-3







     def 							lowercase__       (						self :						int      ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		scheduler_class(**_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		[1_06, 0]
           scheduler.set_timesteps(timesteps=_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		scheduler.timesteps

           SCREAMING_SNAKE_CASE__       :  Dict						     =		torch.manual_seed(0      )

           SCREAMING_SNAKE_CASE__       :  Any						     =		self.dummy_model()
           SCREAMING_SNAKE_CASE__       :  Any						     =		self.dummy_sample_deter * scheduler.init_noise_sigma

           for t in timesteps:
                 # 1. scale model input
                 SCREAMING_SNAKE_CASE__       :  int						     =		scheduler.scale_model_input(_lowercase   ,   _lowercase      )

                 # 2. predict noise residual
                 SCREAMING_SNAKE_CASE__       :  int						     =		model(_lowercase   ,   _lowercase      )

                 # 3. predict previous sample x_t-1
                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		scheduler.step(_lowercase   ,   _lowercase   ,   _lowercase   ,   generator=_lowercase      ).prev_sample

                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		pred_prev_sample

           SCREAMING_SNAKE_CASE__       :  int						     =		torch.sum(torch.abs(_lowercase      )      )
           SCREAMING_SNAKE_CASE__       :  Dict						     =		torch.mean(torch.abs(_lowercase      )      )

           assert abs(result_sum.item() - 347.6357      ) < 1E-2
           assert abs(result_mean.item() - 0.4527      ) < 1E-3







     def 							lowercase__       (						self :						Any      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  int						     =		self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__       :  str						     =		scheduler_class(**_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		[39, 30, 12, 15, 0]

           with self.assertRaises(_lowercase   ,   msg='''`timesteps` must be in descending order.'''      ):
                 scheduler.set_timesteps(timesteps=_lowercase      )







     def 							lowercase__       (						self :						Optional[Any]      ):
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  str						     =		self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__       :  Any						     =		scheduler_class(**_lowercase      )

           SCREAMING_SNAKE_CASE__       :  int						     =		[39, 30, 12, 1, 0]
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		len(_lowercase      )

           with self.assertRaises(_lowercase   ,   msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''      ):
                 scheduler.set_timesteps(num_inference_steps=_lowercase   ,   timesteps=_lowercase      )







     def 							lowercase__       (						self :						Any      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__       :  int						     =		scheduler_class(**_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Dict						     =		[scheduler.config.num_train_timesteps]

           with self.assertRaises(
               _lowercase   ,   msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''   ,   ):
                 scheduler.set_timesteps(timesteps=_lowercase      )








import math




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int)   ->       str:






							'''simple docstring'''





							__UpperCamelCase      :  Union[str, Any]       =   0
							__UpperCamelCase      :  int       =   0
							while num > 0:
														__UpperCamelCase      :  List[Any]       =   num % 8
														__UpperCamelCase      :  Tuple       =   octal + (remainder * math.floor(math.pow(10		,    _lowerCamelCase)))
														counter += 1
														__UpperCamelCase      :  Optional[Any]       =   math.floor(num / 8)  # basically /= 8 without remainder if any
														# This formatting removes trailing '.0' from `octal`.
							return F'0o{int(_lowerCamelCase)}'




def       _SCREAMING_SNAKE_CASE   (						)   ->       None:






							'''simple docstring'''





							print("\n2 in octal is:")
							print(decimal_to_octal(2))  # = 2
							print("\n8 in octal is:")
							print(decimal_to_octal(8))  # = 10
							print("\n65 in octal is:")
							print(decimal_to_octal(65))  # = 101
							print("\n216 in octal is:")
							print(decimal_to_octal(216))  # = 330
							print("\n512 in octal is:")
							print(decimal_to_octal(512))  # = 1000
							print("\n")


if __name__ == "__main__":
		main()